Text samples captured in natural scenes often come from different scripts written in various directions, resulting in significant diversities in character set, orientation, and text length. Current methods handle such diversities by routing input images to corresponding (sub)modules with hardwired rules that are usually tied to image aspect-ratio ranges. However, setting hardwired rules requires extensive knowledge of the testing data and model-building experience, hence is often suboptimal. To resolve this, we propose an end-to-end trainable watch-and-act (WnA) framework, which first watches thumbnails to generate a routing plan and then routes the samples to corresponding experts to produce the recognition results. The framework shows a significant robustness improvement over the corresponding rule-based baselines by 2% of line accuracy on the recent multi-orientation open-set text recognition benchmark. The proposed framework, as a system, also shows 3–10% line accuracy advantages over previous open-set text recognition methods on horizontal samples. 

We propose a method to improve the overall accuracy of a neural network for predicting a sequence without using more training data nor adding more parameters. We apply a center loss at the sequence level as an auxiliary task. At every epoch we compute the center for each class, then we apply a center loss on each element of the sequence in order to reduce the intra-class distance. Center loss makes features more discriminative as well as compact in the feature space which increases the accuracy of the network and reduces overfitting. The network is trained jointly with the sequence prediction task and the center loss auxiliary task which increases the computation time only during training not in inference. We evaluate our method in a handwriting text recognition context on seven datasets. In addition to outperforming methods that do not use additional data for all datasets, our method achieves competitive results compared to those that do, with faster inference speed and fewer parameters. We also show that our method applied on a light neural network improves accuracy and is able to achieve competitive performance compared to deeper models. The advantage of using a light model is the processing speed needed for real applications. Code is available at https://github.com/simon-corbi/htr-ijcnn-2025.

This six-volume set LNCS 14804-14809 constitutes the proceedings of the 18th International Conference on Document Analysis and Recognition, ICDAR 2024, held in Athens, Greece, during August 30–September 4, 2024.The total of 144 full papers presented in these proceedings were carefully selected from 263 submissions.The papers reflect topics such as: Document image processing; physical and logical layout analysis; text and symbol recognition; handwriting recognition; document analysis systems; document classification; indexing and retrieval of documents; document synthesis; extracting document semantics; NLP for document understanding; office automation; graphics recognition; human document interaction; document representation modeling and much more.

This six-volume set LNCS 14804-14809 constitutes the proceedings of the 18th International Conference on Document Analysis and Recognition, ICDAR 2024, held in Athens, Greece, during August 30–September 4, 2024.The total of 144 full papers presented in these proceedings were carefully selected from 263 submissions.The papers reflect topics such as: Document image processing; physical and logical layout analysis; text and symbol recognition; handwriting recognition; document analysis systems; document classification; indexing and retrieval of documents; document synthesis; extracting document semantics; NLP for document understanding; office automation; graphics recognition; human document interaction; document representation modeling and much more.

This six-volume set LNCS 14804-14809 constitutes the proceedings of the 18th International Conference on Document Analysis and Recognition, ICDAR 2024, held in Athens, Greece, during August 30–September 4, 2024.The total of 144 full papers presented in these proceedings were carefully selected from 263 submissions.The papers reflect topics such as: Document image processing; physical and logical layout analysis; text and symbol recognition; handwriting recognition; document analysis systems; document classification; indexing and retrieval of documents; document synthesis; extracting document semantics; NLP for document understanding; office automation; graphics recognition; human document interaction; document representation modeling and much more.

This six-volume set LNCS 14804-14809 constitutes the proceedings of the 18th International Conference on Document Analysis and Recognition, ICDAR 2024, held in Athens, Greece, during August 30–September 4, 2024.The total of 144 full papers presented in these proceedings were carefully selected from 263 submissions.The papers reflect topics such as: Document image processing; physical and logical layout analysis; text and symbol recognition; handwriting recognition; document analysis systems; document classification; indexing and retrieval of documents; document synthesis; extracting document semantics; NLP for document understanding; office automation; graphics recognition; human document interaction; document representation modeling and much more.

This six-volume set LNCS 14804-14809 constitutes the proceedings of the 18th International Conference on Document Analysis and Recognition, ICDAR 2024, held in Athens, Greece, during August 30–September 4, 2024.The total of 144 full papers presented in these proceedings were carefully selected from 263 submissions.The papers reflect topics such as: Document image processing; physical and logical layout analysis; text and symbol recognition; handwriting recognition; document analysis systems; document classification; indexing and retrieval of documents; document synthesis; extracting document semantics; NLP for document understanding; office automation; graphics recognition; human document interaction; document representation modeling and much more.

This six-volume set LNCS 14804-14809 constitutes the proceedings of the 18th International Conference on Document Analysis and Recognition, ICDAR 2024, held in Athens, Greece, during August 30–September 4, 2024.The total of 144 full papers presented in these proceedings were carefully selected from 263 submissions.The papers reflect topics such as: Document image processing; physical and logical layout analysis; text and symbol recognition; handwriting recognition; document analysis systems; document classification; indexing and retrieval of documents; document synthesis; extracting document semantics; NLP for document understanding; office automation; graphics recognition; human document interaction; document representation modeling and much more.